System Quality Rating Model

Looking at the data

We plot the data and can see that there is no obvious large difference between the debt levels or scenarios.

Per debt level

data.frame(
  High_debt_version = d.both_completed %>%
    filter(high_debt_version == "true") %>%
    pull(quality_pre_task) %>%
    revalue(c(
      "-3"="Very Bad",
      "-2"="Bad",
      "-1"="Somewhat Bad",
      "0"="Neutral",
      "1"="Somewhat Good",
      "2"="Good",
      "3"="Very Good"
    )),
  Low_debt_version = d.both_completed %>%
    filter(high_debt_version == "false") %>%
    pull(quality_pre_task) %>%
    revalue(c(
      "-3"="Very Bad",
      "-2"="Bad",
      "-1"="Somewhat Bad",
      "0"="Neutral",
      "1"="Somewhat Good",
      "2"="Good",
      "3"="Very Good"
    ))
) %>%
  likert() %>%
  plot(
    type="density",
    facet = TRUE,
  )

Per scenario

data.frame(
  Tickets = d.both_completed %>%
    filter(scenario == "tickets") %>%
    pull(quality_pre_task) %>%
    revalue(c(
      "-3"="Very Bad",
      "-2"="Bad",
      "-1"="Somewhat Bad",
      "0"="Neutral",
      "1"="Somewhat Good",
      "2"="Good",
      "3"="Very Good"
    )),
  Booking = d.both_completed %>%
    filter(scenario == "booking") %>%
    pull(quality_pre_task) %>%
    revalue(c(
      "-3"="Very Bad",
      "-2"="Bad",
      "-1"="Somewhat Bad",
      "0"="Neutral",
      "1"="Somewhat Good",
      "2"="Good",
      "3"="Very Good"
    ))
) %>%
  likert() %>%
  plot(
    type="density",
    facet = TRUE,
  )

Initial model

As the data is collected from a likert scale we will use a cumulative family, indicating that each level on the scale is an incremental step. This model is also able to fit the data well.

We include high_debt_verison as a predictor in our model as this variable represent the very effect we want to measure. We also include a varying intercept for each individual to prevent the model from learning too much from single participants with extreme measurements.

Selecting priors

We iterate over the model until we have sane priors.

Base model with priors

scenario_quality.with <- extendable_model(
  base_name = "scenario_quality",
  base_formula = "quality_pre_task ~ 1 + high_debt_version + (1 | session)",
  base_priors = c(
    prior(normal(0, 1), class = "b"),
    prior(normal(0, 2), class = "Intercept"),
    prior(exponential(1), class = "sd")
  ),
  family = cumulative(),
  data = d.both_completed,
)

Default priors

prior_summary(scenario_quality.with(only_priors= TRUE))

Selected priors

prior_summary(scenario_quality.with(sample_prior = "only"))

Prior predictive check

pp_check(scenario_quality.with(sample_prior = "only"), nsamples = 200, type = "bars")

Beta parameter influence

We choose a beta parameter priors allowing for the beta parameter to account for 100% of the effect but that is skeptical to such strong effects from the beta parameter.

sim.size <- 1000
sim.intercept <- rnorm(sim.size, 0, 2)
sim.beta <- rnorm(sim.size, 0, 1)
sim.beta.diff <- (plogis(sim.intercept + sim.beta) / plogis(sim.intercept) * 100) - 100

data.frame(x = sim.beta.diff) %>%
  ggplot(aes(x)) +
  geom_density() +
  xlim(-150, 150) +
  labs(
    title = "Beta parameter prior influence",
    x = "Estimate with beta as % of estimate without beta",
    y = "Density"
  )

Model fit

We check the posterior distribution and can see that the model seems to have been able to fit the data well. Sampling seems to also have worked well as Rhat values are close to 1 and the sampling plots look nice.

Posterior predictive check

pp_check(scenario_quality.with(), nsamples = 200, type = "bars")

Summary

summary(scenario_quality.with())

##  Family: cumulative 
##   Links: mu = logit; disc = identity 
## Formula: quality_pre_task ~ 1 + high_debt_version + (1 | session) 
##    Data: as.data.frame(data) (Number of observations: 44) 
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 4000
## 
## Group-Level Effects: 
## ~session (Number of levels: 22) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.37      0.30     0.01     1.12 1.00     1955     1935
## 
## Population-Level Effects: 
##                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept[1]              -1.93      0.54    -3.08    -0.98 1.00     4023
## Intercept[2]              -0.57      0.41    -1.41     0.21 1.00     5684
## Intercept[3]               0.22      0.41    -0.58     1.02 1.00     5406
## Intercept[4]               0.61      0.42    -0.17     1.42 1.00     5223
## Intercept[5]               1.91      0.49     1.02     2.91 1.00     4828
## Intercept[6]               3.97      0.72     2.65     5.51 1.00     5686
## high_debt_versionfalse     1.37      0.50     0.40     2.36 1.00     5604
##                        Tail_ESS
## Intercept[1]               2860
## Intercept[2]               3484
## Intercept[3]               3796
## Intercept[4]               3670
## Intercept[5]               3379
## Intercept[6]               3075
## high_debt_versionfalse     2993
## 
## Family Specific Parameters: 
##      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## disc     1.00      0.00     1.00     1.00 1.00     4000     4000
## 
## Samples were drawn using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Sampling plots

plot(scenario_quality.with(), ask = FALSE)

Model predictor extenstions

# default prior for monotonic predictor
edlvl_prior <- prior(dirichlet(2), class = "simo", coef = "moeducation_level1")

We use loo to check some possible extensions on the model.

One variable

loo_result <- loo(
  # Benchmark model(s)
  scenario_quality.with(),
  
  # New model(s)
  scenario_quality.with("work_domain"),
  scenario_quality.with("work_experience_programming.s"),
  scenario_quality.with("work_experience_java.s"),
  scenario_quality.with("education_field"),
  scenario_quality.with("mo(education_level)", edlvl_prior),
  scenario_quality.with("workplace_peer_review"),
  scenario_quality.with("workplace_td_tracking"),
  scenario_quality.with("workplace_pair_programming"),
  scenario_quality.with("workplace_coding_standards"),
  scenario_quality.with("scenario"),
  scenario_quality.with("group")
)

Comparison

loo_result[2]

## $diffs
##                                                           elpd_diff se_diff
## scenario_quality.with("work_experience_programming.s")     0.0       0.0   
## scenario_quality.with("workplace_coding_standards")       -0.2       1.7   
## scenario_quality.with("workplace_peer_review")            -0.5       1.7   
## scenario_quality.with("work_experience_java.s")           -0.5       0.5   
## scenario_quality.with()                                   -1.1       1.7   
## scenario_quality.with("mo(education_level)", edlvl_prior) -1.2       1.6   
## scenario_quality.with("workplace_td_tracking")            -1.6       1.5   
## scenario_quality.with("workplace_pair_programming")       -1.7       1.7   
## scenario_quality.with("scenario")                         -1.8       1.8   
## scenario_quality.with("education_field")                  -2.1       1.8   
## scenario_quality.with("group")                            -2.1       1.6   
## scenario_quality.with("work_domain")                      -2.8       2.0

Diagnostics

loo_result[1]

## $loos
## $loos$`scenario_quality.with()`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.4 4.1
## p_loo         8.5 0.9
## looic       162.7 8.2
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1728      
##  (0.5, 0.7]   (ok)        1     2.3%   1455      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_domain")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -83.0 4.4
## p_loo        12.9 1.6
## looic       166.0 8.8
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   864       
##  (0.5, 0.7]   (ok)        1     2.3%   1024      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_experience_programming.s")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.2 4.2
## p_loo         9.1 0.9
## looic       160.4 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1939      
##  (0.5, 0.7]   (ok)        2     4.5%   1859      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_experience_java.s")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.2
## p_loo         9.4 1.0
## looic       161.5 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     39    88.6%   1511      
##  (0.5, 0.7]   (ok)        5    11.4%   1722      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("education_field")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -82.3 4.1
## p_loo        10.1 1.1
## looic       164.7 8.2
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("mo(education_level)", edlvl_prior)`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.4 4.2
## p_loo         9.4 1.0
## looic       162.9 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1584      
##  (0.5, 0.7]   (ok)        1     2.3%   3657      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_peer_review")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.3
## p_loo         8.9 0.9
## looic       161.4 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1777      
##  (0.5, 0.7]   (ok)        2     4.5%   4871      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_td_tracking")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.8 4.0
## p_loo         9.3 0.9
## looic       163.7 8.1
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_pair_programming")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.9 4.2
## p_loo         9.6 1.0
## looic       163.8 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_coding_standards")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.4 4.2
## p_loo         9.0 0.9
## looic       160.8 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1678      
##  (0.5, 0.7]   (ok)        2     4.5%   1895      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("scenario")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -82.0 4.0
## p_loo         9.2 0.9
## looic       164.0 8.0
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1511      
##  (0.5, 0.7]   (ok)        2     4.5%   3203      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("group")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -82.4 4.3
## p_loo        10.6 1.1
## looic       164.7 8.7
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.

Two variables

loo_result <- loo(
  # Benchmark model(s)
  scenario_quality.with(),
  scenario_quality.with("work_experience_programming.s"),
  scenario_quality.with("workplace_coding_standards"),
  scenario_quality.with("workplace_peer_review"),
  scenario_quality.with("work_experience_java.s"),
  
  # New model(s)
  scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards")),
  scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review")),
  scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s")),
  
  scenario_quality.with(c("workplace_coding_standards", "workplace_peer_review")),
  scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s")),
  
  scenario_quality.with(c("workplace_peer_review", "work_experience_java.s"))
)

Comparison

loo_result[2]

## $diffs
##                                                                                         elpd_diff
## scenario_quality.with("work_experience_programming.s")                                   0.0     
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards")) -0.1     
## scenario_quality.with("workplace_coding_standards")                                     -0.2     
## scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review"))      -0.4     
## scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s"))     -0.4     
## scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s"))        -0.5     
## scenario_quality.with("workplace_peer_review")                                          -0.5     
## scenario_quality.with("work_experience_java.s")                                         -0.5     
## scenario_quality.with(c("workplace_coding_standards", "workplace_peer_review"))         -0.7     
## scenario_quality.with(c("workplace_peer_review", "work_experience_java.s"))             -0.7     
## scenario_quality.with()                                                                 -1.1     
##                                                                                         se_diff
## scenario_quality.with("work_experience_programming.s")                                   0.0   
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards"))  0.9   
## scenario_quality.with("workplace_coding_standards")                                      1.7   
## scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review"))       0.7   
## scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s"))      0.3   
## scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s"))         1.0   
## scenario_quality.with("workplace_peer_review")                                           1.7   
## scenario_quality.with("work_experience_java.s")                                          0.5   
## scenario_quality.with(c("workplace_coding_standards", "workplace_peer_review"))          1.7   
## scenario_quality.with(c("workplace_peer_review", "work_experience_java.s"))              0.9   
## scenario_quality.with()                                                                  1.7

Diagnostics

loo_result[1]

## $loos
## $loos$`scenario_quality.with()`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.4 4.1
## p_loo         8.5 0.9
## looic       162.7 8.2
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1728      
##  (0.5, 0.7]   (ok)        1     2.3%   1455      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_experience_programming.s")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.2 4.2
## p_loo         9.1 0.9
## looic       160.4 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1939      
##  (0.5, 0.7]   (ok)        2     4.5%   1859      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_coding_standards")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.4 4.2
## p_loo         9.0 0.9
## looic       160.8 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1678      
##  (0.5, 0.7]   (ok)        2     4.5%   1895      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_peer_review")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.3
## p_loo         8.9 0.9
## looic       161.4 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1777      
##  (0.5, 0.7]   (ok)        2     4.5%   4871      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_experience_java.s")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.2
## p_loo         9.4 1.0
## looic       161.5 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     39    88.6%   1511      
##  (0.5, 0.7]   (ok)        5    11.4%   1722      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.3 4.3
## p_loo         9.7 0.9
## looic       160.6 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     41    93.2%   1818      
##  (0.5, 0.7]   (ok)        3     6.8%   1983      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.6 4.4
## p_loo         9.9 1.0
## looic       161.2 8.8
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.6 4.2
## p_loo         9.5 1.0
## looic       161.3 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1158      
##  (0.5, 0.7]   (ok)        2     4.5%   1648      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("workplace_coding_standards", "workplace_peer_review"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.9 4.3
## p_loo         9.7 1.1
## looic       161.8 8.7
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1632      
##  (0.5, 0.7]   (ok)        1     2.3%   1812      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.3
## p_loo         9.9 1.0
## looic       161.4 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1787      
##  (0.5, 0.7]   (ok)        1     2.3%   1657      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("workplace_peer_review", "work_experience_java.s"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.9 4.4
## p_loo         9.9 1.0
## looic       161.9 8.8
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     38    86.4%   1799      
##  (0.5, 0.7]   (ok)        6    13.6%   1221      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.

Three variables

loo_result <- loo(
  # Benchmark model(s)
  scenario_quality.with(),
  
  scenario_quality.with("work_experience_programming.s"),
  scenario_quality.with("workplace_coding_standards"),
  scenario_quality.with("workplace_peer_review"),
  scenario_quality.with("work_experience_java.s"),
  
  scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards")),
  scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review")),
  scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s")),
  
  # New model(s)
  scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards", "workplace_peer_review")),
  scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards", "work_experience_java.s")),
  scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s", "workplace_peer_review")),
  scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s", "workplace_peer_review"))
)

Comparison

loo_result[2]

## $diffs
##                                                                                                                       elpd_diff
## scenario_quality.with("work_experience_programming.s")                                                                 0.0     
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards"))                               -0.1     
## scenario_quality.with("workplace_coding_standards")                                                                   -0.2     
## scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review"))                                    -0.4     
## scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s"))                                      -0.5     
## scenario_quality.with("workplace_peer_review")                                                                        -0.5     
## scenario_quality.with("work_experience_java.s")                                                                       -0.5     
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards",     "work_experience_java.s")) -0.5     
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards",     "workplace_peer_review"))  -0.8     
## scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s",     "workplace_peer_review"))         -0.9     
## scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s",     "workplace_peer_review"))      -1.0     
## scenario_quality.with()                                                                                               -1.1     
##                                                                                                                       se_diff
## scenario_quality.with("work_experience_programming.s")                                                                 0.0   
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards"))                                0.9   
## scenario_quality.with("workplace_coding_standards")                                                                    1.7   
## scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review"))                                     0.7   
## scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s"))                                       1.0   
## scenario_quality.with("workplace_peer_review")                                                                         1.7   
## scenario_quality.with("work_experience_java.s")                                                                        0.5   
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards",     "work_experience_java.s"))  0.9   
## scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards",     "workplace_peer_review"))   1.0   
## scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s",     "workplace_peer_review"))          1.0   
## scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s",     "workplace_peer_review"))       0.7   
## scenario_quality.with()                                                                                                1.7

Diagnostics

loo_result[1]

## $loos
## $loos$`scenario_quality.with()`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.4 4.1
## p_loo         8.5 0.9
## looic       162.7 8.2
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1728      
##  (0.5, 0.7]   (ok)        1     2.3%   1455      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_experience_programming.s")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.2 4.2
## p_loo         9.1 0.9
## looic       160.4 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1939      
##  (0.5, 0.7]   (ok)        2     4.5%   1859      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_coding_standards")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.4 4.2
## p_loo         9.0 0.9
## looic       160.8 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1678      
##  (0.5, 0.7]   (ok)        2     4.5%   1895      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("workplace_peer_review")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.3
## p_loo         8.9 0.9
## looic       161.4 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   1777      
##  (0.5, 0.7]   (ok)        2     4.5%   4871      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with("work_experience_java.s")`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.2
## p_loo         9.4 1.0
## looic       161.5 8.4
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     39    88.6%   1511      
##  (0.5, 0.7]   (ok)        5    11.4%   1722      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.3 4.3
## p_loo         9.7 0.9
## looic       160.6 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     41    93.2%   1818      
##  (0.5, 0.7]   (ok)        3     6.8%   1983      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "workplace_peer_review"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.6 4.4
## p_loo         9.9 1.0
## looic       161.2 8.8
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.3
## p_loo         9.9 1.0
## looic       161.4 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1787      
##  (0.5, 0.7]   (ok)        1     2.3%   1657      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards",     "workplace_peer_review"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.0 4.4
## p_loo        10.6 1.1
## looic       162.0 8.8
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "workplace_coding_standards",     "work_experience_java.s"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -80.7 4.3
## p_loo        10.1 1.0
## looic       161.5 8.6
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     42    95.5%   944       
##  (0.5, 0.7]   (ok)        2     4.5%   2540      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("work_experience_programming.s", "work_experience_java.s",     "workplace_peer_review"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.2 4.4
## p_loo        10.4 1.0
## looic       162.4 8.8
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1057      
##  (0.5, 0.7]   (ok)        1     2.3%   1522      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## 
## $loos$`scenario_quality.with(c("workplace_coding_standards", "work_experience_java.s",     "workplace_peer_review"))`
## 
## Computed from 4000 by 44 log-likelihood matrix
## 
##          Estimate  SE
## elpd_loo    -81.1 4.4
## p_loo        10.5 1.1
## looic       162.3 8.7
## ------
## Monte Carlo SE of elpd_loo is 0.1.
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. n_eff
## (-Inf, 0.5]   (good)     43    97.7%   1606      
##  (0.5, 0.7]   (ok)        1     2.3%   1766      
##    (0.7, 1]   (bad)       0     0.0%   <NA>      
##    (1, Inf)   (very bad)  0     0.0%   <NA>      
## 
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.

Candidate models

We pick some of our top performing models as candidates and inspect them closer.

The candidate models are named and listed in order of complexity.

ScenarioQuality0

We select the simplest model as a baseline.

scenario_quality0 <- brm(
  "quality_pre_task ~ 1 + high_debt_version + (1 | session)",
  prior = c(
    prior(normal(0, 1), class = "b"),
    prior(normal(0, 2), class = "Intercept"),
    prior(exponential(1), class = "sd")
  ),
  family = cumulative(),
  data = as.data.frame(d.both_completed),
  file = "fits/scenario_quality0",
  file_refit = "on_change",
  seed = 20210421
)

Summary

summary(scenario_quality0)

##  Family: cumulative 
##   Links: mu = logit; disc = identity 
## Formula: quality_pre_task ~ 1 + high_debt_version + (1 | session) 
##    Data: as.data.frame(d.both_completed) (Number of observations: 44) 
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 4000
## 
## Group-Level Effects: 
## ~session (Number of levels: 22) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.37      0.30     0.01     1.12 1.00     1955     1935
## 
## Population-Level Effects: 
##                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept[1]              -1.93      0.54    -3.08    -0.98 1.00     4023
## Intercept[2]              -0.57      0.41    -1.41     0.21 1.00     5684
## Intercept[3]               0.22      0.41    -0.58     1.02 1.00     5406
## Intercept[4]               0.61      0.42    -0.17     1.42 1.00     5223
## Intercept[5]               1.91      0.49     1.02     2.91 1.00     4828
## Intercept[6]               3.97      0.72     2.65     5.51 1.00     5686
## high_debt_versionfalse     1.37      0.50     0.40     2.36 1.00     5604
##                        Tail_ESS
## Intercept[1]               2860
## Intercept[2]               3484
## Intercept[3]               3796
## Intercept[4]               3670
## Intercept[5]               3379
## Intercept[6]               3075
## high_debt_versionfalse     2993
## 
## Family Specific Parameters: 
##      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## disc     1.00      0.00     1.00     1.00 1.00     4000     4000
## 
## Samples were drawn using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Random effects

ranef(scenario_quality0)

## $session
## , , Intercept
## 
##                              Estimate Est.Error       Q2.5     Q97.5
## 6033d69a5af2c702367b3a95 -0.145808643 0.4248984 -1.1949402 0.5746195
## 6033d90a5af2c702367b3a96  0.010889353 0.4027784 -0.8648566 0.9186598
## 6034fc165af2c702367b3a98 -0.014139016 0.3898703 -0.8812689 0.8228452
## 603500725af2c702367b3a99  0.278401903 0.5592082 -0.4480578 1.8078078
## 603f97625af2c702367b3a9d -0.025614765 0.4025079 -0.9741733 0.8118892
## 603fd5d95af2c702367b3a9e  0.029689046 0.4237631 -0.8545135 0.9521520
## 60409b7b5af2c702367b3a9f -0.041044832 0.3932839 -0.9830847 0.7521488
## 604b82b5a7718fbed181b336  0.229636160 0.5094315 -0.5089644 1.5880900
## 6050c1bf856f36729d2e5218 -0.106199391 0.4382125 -1.2141728 0.6825527
## 6050e1e7856f36729d2e5219  0.009089846 0.4345471 -0.9334836 0.9755679
## 6055fdc6856f36729d2e521b  0.008980702 0.3847572 -0.8205482 0.8926897
## 60589862856f36729d2e521f -0.150708085 0.4433861 -1.2909802 0.5624619
## 605afa3a856f36729d2e5222 -0.015244970 0.3923156 -0.8588107 0.8227719
## 605c8bc6856f36729d2e5223 -0.152145038 0.4485480 -1.3162503 0.5817486
## 605f3f2d856f36729d2e5224 -0.154915352 0.4605884 -1.3723010 0.5803257
## 605f46c3856f36729d2e5225  0.067034998 0.4079185 -0.7292448 1.0766500
## 60605337856f36729d2e5226 -0.083013356 0.4024577 -1.0944837 0.7104604
## 60609ae6856f36729d2e5228  0.003131549 0.4121036 -0.9170141 0.9062533
## 6061ce91856f36729d2e522e  0.015189729 0.4217685 -0.8343022 0.9733779
## 6061f106856f36729d2e5231  0.241676436 0.4979721 -0.4188072 1.6213750
## 6068ea9f856f36729d2e523e -0.059896999 0.4270294 -1.0585490 0.7845769
## 6075ab05856f36729d2e5247  0.070761429 0.4008793 -0.7451969 1.0260368

Sampling plots

plot(scenario_quality0, ask = FALSE)

Posterior predictive check

pp_check(scenario_quality0, nsamples = 200, type = "bars")

ScenarioQuality1

We select the best performing model with one variable.

scenario_quality1 <- brm(
  "quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + (1 | session)",
  prior = c(
    prior(normal(0, 1), class = "b"),
    prior(normal(0, 2), class = "Intercept"),
    prior(exponential(1), class = "sd")
  ),
  family = cumulative(),
  data = as.data.frame(d.both_completed),
  file = "fits/scenario_quality1",
  file_refit = "on_change",
  seed = 20210421
)

Summary

summary(scenario_quality1)

##  Family: cumulative 
##   Links: mu = logit; disc = identity 
## Formula: quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + (1 | session) 
##    Data: as.data.frame(d.both_completed) (Number of observations: 44) 
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 4000
## 
## Group-Level Effects: 
## ~session (Number of levels: 22) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.34      0.28     0.01     1.02 1.00     1996     1992
## 
## Population-Level Effects: 
##                               Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept[1]                     -1.96      0.54    -3.07    -0.97 1.00
## Intercept[2]                     -0.54      0.42    -1.37     0.25 1.00
## Intercept[3]                      0.25      0.41    -0.58     1.05 1.00
## Intercept[4]                      0.65      0.43    -0.20     1.48 1.00
## Intercept[5]                      1.98      0.48     1.07     2.94 1.00
## Intercept[6]                      4.09      0.72     2.79     5.58 1.00
## high_debt_versionfalse            1.46      0.50     0.49     2.46 1.00
## work_experience_programming.s    -0.54      0.30    -1.14     0.02 1.00
##                               Bulk_ESS Tail_ESS
## Intercept[1]                      4001     3187
## Intercept[2]                      5984     3454
## Intercept[3]                      5672     3254
## Intercept[4]                      5190     3618
## Intercept[5]                      4921     3641
## Intercept[6]                      5569     3280
## high_debt_versionfalse            6900     2831
## work_experience_programming.s     5667     3206
## 
## Family Specific Parameters: 
##      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## disc     1.00      0.00     1.00     1.00 1.00     4000     4000
## 
## Samples were drawn using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Random effects

ranef(scenario_quality1)

## $session
## , , Intercept
## 
##                              Estimate Est.Error       Q2.5     Q97.5
## 6033d69a5af2c702367b3a95 -0.159433967 0.4222374 -1.2195278 0.5324355
## 6033d90a5af2c702367b3a96 -0.015543701 0.3761998 -0.8638936 0.7832535
## 6034fc165af2c702367b3a98 -0.042729862 0.3738382 -0.9355357 0.7190091
## 603500725af2c702367b3a99  0.234581683 0.5184966 -0.4245614 1.7083458
## 603f97625af2c702367b3a9d -0.062785160 0.3935959 -1.0109665 0.6876793
## 603fd5d95af2c702367b3a9e  0.028150330 0.4083989 -0.8415633 1.0003215
## 60409b7b5af2c702367b3a9f -0.066476703 0.3818330 -1.0111193 0.6554037
## 604b82b5a7718fbed181b336  0.202113370 0.4712225 -0.4841701 1.4651678
## 6050c1bf856f36729d2e5218 -0.091786068 0.4058363 -1.0932963 0.6303960
## 6050e1e7856f36729d2e5219  0.006725887 0.4233766 -0.9116780 0.9146043
## 6055fdc6856f36729d2e521b -0.014445895 0.3954740 -0.9209347 0.8258082
## 60589862856f36729d2e521f -0.036633405 0.4128405 -1.0130460 0.7965760
## 605afa3a856f36729d2e5222  0.071220522 0.3932498 -0.6663469 1.0517088
## 605c8bc6856f36729d2e5223 -0.131112297 0.4220846 -1.2187647 0.5509834
## 605f3f2d856f36729d2e5224 -0.001081396 0.4104092 -0.9145712 0.8871584
## 605f46c3856f36729d2e5225  0.035923735 0.3825851 -0.7322183 0.8832729
## 60605337856f36729d2e5226 -0.103826004 0.3974821 -1.0814420 0.6448245
## 60609ae6856f36729d2e5228 -0.020517043 0.3960141 -0.8910282 0.8307672
## 6061ce91856f36729d2e522e -0.011281013 0.3956216 -0.9082552 0.8364204
## 6061f106856f36729d2e5231  0.182777387 0.4538511 -0.4902734 1.3889143
## 6068ea9f856f36729d2e523e -0.054519571 0.3972178 -1.0131963 0.7438614
## 6075ab05856f36729d2e5247  0.043030243 0.3823530 -0.7348279 0.9251660

Sampling plots

plot(scenario_quality1, ask = FALSE)

Posterior predictive check

pp_check(scenario_quality1, nsamples = 200, type = "bars")

ScenarioQuality2

We select the best performing model with two variables.

scenario_quality2 <- brm(
  "quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + workplace_coding_standards + (1 | session)",
  prior = c(
    prior(normal(0, 1), class = "b"),
    prior(normal(0, 2), class = "Intercept"),
    prior(exponential(1), class = "sd")
  ),
  family = cumulative(),
  data = as.data.frame(d.both_completed),
  file = "fits/scenario_quality2",
  file_refit = "on_change",
  seed = 20210421
)

Summary

summary(scenario_quality2)

##  Family: cumulative 
##   Links: mu = logit; disc = identity 
## Formula: quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + workplace_coding_standards + (1 | session) 
##    Data: as.data.frame(d.both_completed) (Number of observations: 44) 
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 4000
## 
## Group-Level Effects: 
## ~session (Number of levels: 22) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.34      0.28     0.01     1.05 1.00     2092     1852
## 
## Population-Level Effects: 
##                                 Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept[1]                       -1.74      0.59    -2.96    -0.66 1.00
## Intercept[2]                       -0.31      0.47    -1.23     0.61 1.00
## Intercept[3]                        0.49      0.48    -0.45     1.44 1.00
## Intercept[4]                        0.90      0.49    -0.07     1.85 1.00
## Intercept[5]                        2.25      0.54     1.20     3.28 1.00
## Intercept[6]                        4.39      0.78     2.95     6.00 1.00
## high_debt_versionfalse              1.46      0.50     0.47     2.44 1.00
## work_experience_programming.s      -0.44      0.32    -1.08     0.19 1.00
## workplace_coding_standardsfalse     0.55      0.53    -0.48     1.58 1.00
##                                 Bulk_ESS Tail_ESS
## Intercept[1]                        5001     3217
## Intercept[2]                        6046     3513
## Intercept[3]                        5123     3417
## Intercept[4]                        4818     3232
## Intercept[5]                        4548     3319
## Intercept[6]                        5731     3224
## high_debt_versionfalse              5943     2983
## work_experience_programming.s       5691     3066
## workplace_coding_standardsfalse     5608     3186
## 
## Family Specific Parameters: 
##      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## disc     1.00      0.00     1.00     1.00 1.00     4000     4000
## 
## Samples were drawn using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Random effects

ranef(scenario_quality2)

## $session
## , , Intercept
## 
##                               Estimate Est.Error       Q2.5     Q97.5
## 6033d69a5af2c702367b3a95 -0.1850866386 0.4384291 -1.3340180 0.4855577
## 6033d90a5af2c702367b3a96  0.0075140476 0.3863993 -0.8321943 0.8579651
## 6034fc165af2c702367b3a98 -0.0701978961 0.3758943 -0.9578170 0.6824377
## 603500725af2c702367b3a99  0.2130600807 0.4954732 -0.4602421 1.5466533
## 603f97625af2c702367b3a9d -0.0281558797 0.3732357 -0.9172471 0.7497192
## 603fd5d95af2c702367b3a9e  0.0085768526 0.4000502 -0.8736815 0.8766696
## 60409b7b5af2c702367b3a9f -0.0909559256 0.3848050 -1.0619832 0.5911907
## 604b82b5a7718fbed181b336  0.1684272787 0.4480970 -0.5132888 1.3873255
## 6050c1bf856f36729d2e5218 -0.0575260636 0.3670973 -0.9456209 0.6788483
## 6050e1e7856f36729d2e5219  0.0007994078 0.4386599 -0.9292900 0.9777997
## 6055fdc6856f36729d2e521b  0.0170605966 0.3979439 -0.8282522 0.9324863
## 60589862856f36729d2e521f -0.0363886288 0.3907343 -0.9545415 0.7499846
## 605afa3a856f36729d2e5222  0.0960886516 0.4021753 -0.6031806 1.1364945
## 605c8bc6856f36729d2e5223 -0.0962088139 0.4246298 -1.1668190 0.6899267
## 605f3f2d856f36729d2e5224 -0.0123978569 0.4147140 -0.9445681 0.8827722
## 605f46c3856f36729d2e5225  0.0696832011 0.3850693 -0.6216591 1.0197998
## 60605337856f36729d2e5226 -0.0641767609 0.3929412 -1.0205835 0.6657965
## 60609ae6856f36729d2e5228 -0.0507428335 0.4008238 -1.0412842 0.7429305
## 6061ce91856f36729d2e522e  0.0085419736 0.3948507 -0.8539918 0.8687434
## 6061f106856f36729d2e5231  0.1642983208 0.4297256 -0.4812203 1.2917675
## 6068ea9f856f36729d2e523e -0.0759442330 0.4117008 -1.0736785 0.6978153
## 6075ab05856f36729d2e5247  0.0160915146 0.3727621 -0.8039486 0.8304968

Sampling plots

plot(scenario_quality2, ask = FALSE)

Posterior predictive check

pp_check(scenario_quality2, nsamples = 200, type = "bars")

ScenarioQuality3

We select the best performing model with three variables.

scenario_quality3 <- brm(
  "quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + workplace_coding_standards + work_experience_java.s + (1 | session)",
  prior = c(
    prior(normal(0, 1), class = "b"),
    prior(normal(0, 2), class = "Intercept"),
    prior(exponential(1), class = "sd")
  ),
  family = cumulative(),
  data = as.data.frame(d.both_completed),
  file = "fits/scenario_quality3",
  file_refit = "on_change",
  seed = 20210421
)

Summary

summary(scenario_quality3)

##  Family: cumulative 
##   Links: mu = logit; disc = identity 
## Formula: quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + workplace_coding_standards + work_experience_java.s + (1 | session) 
##    Data: as.data.frame(d.both_completed) (Number of observations: 44) 
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 4000
## 
## Group-Level Effects: 
## ~session (Number of levels: 22) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.36      0.29     0.01     1.08 1.00     2460     1877
## 
## Population-Level Effects: 
##                                 Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept[1]                       -1.75      0.59    -2.98    -0.66 1.00
## Intercept[2]                       -0.31      0.47    -1.25     0.59 1.00
## Intercept[3]                        0.50      0.48    -0.46     1.45 1.00
## Intercept[4]                        0.91      0.49    -0.08     1.84 1.00
## Intercept[5]                        2.25      0.55     1.20     3.35 1.00
## Intercept[6]                        4.38      0.80     2.87     6.04 1.00
## high_debt_versionfalse              1.46      0.51     0.44     2.46 1.00
## work_experience_programming.s      -0.37      0.63    -1.62     0.86 1.00
## workplace_coding_standardsfalse     0.56      0.55    -0.51     1.63 1.00
## work_experience_java.s             -0.09      0.62    -1.28     1.17 1.00
##                                 Bulk_ESS Tail_ESS
## Intercept[1]                        5124     3130
## Intercept[2]                        5381     3355
## Intercept[3]                        5023     3567
## Intercept[4]                        4935     3339
## Intercept[5]                        4403     3482
## Intercept[6]                        6132     3348
## high_debt_versionfalse              6472     2923
## work_experience_programming.s       3914     3160
## workplace_coding_standardsfalse     5347     2768
## work_experience_java.s              3896     2977
## 
## Family Specific Parameters: 
##      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## disc     1.00      0.00     1.00     1.00 1.00     4000     4000
## 
## Samples were drawn using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Random effects

ranef(scenario_quality3)

## $session
## , , Intercept
## 
##                               Estimate Est.Error       Q2.5     Q97.5
## 6033d69a5af2c702367b3a95 -0.1903833044 0.4506084 -1.4153100 0.4852661
## 6033d90a5af2c702367b3a96  0.0093025699 0.4186244 -0.8724130 0.9454708
## 6034fc165af2c702367b3a98 -0.0691445991 0.3824515 -0.9737297 0.6678971
## 603500725af2c702367b3a99  0.2311277379 0.5133153 -0.4584094 1.6174950
## 603f97625af2c702367b3a9d -0.0207399348 0.3879605 -0.9110777 0.8062503
## 603fd5d95af2c702367b3a9e  0.0049382755 0.4184704 -0.9122366 0.9282139
## 60409b7b5af2c702367b3a9f -0.1055862073 0.4076941 -1.1604295 0.6124789
## 604b82b5a7718fbed181b336  0.1924915130 0.4605045 -0.4765054 1.4408263
## 6050c1bf856f36729d2e5218 -0.0575276158 0.4013602 -1.0213810 0.7734848
## 6050e1e7856f36729d2e5219  0.0002545732 0.4474630 -0.9747042 1.0211738
## 6055fdc6856f36729d2e521b  0.0053660188 0.4110821 -0.8788615 0.9031909
## 60589862856f36729d2e521f -0.0367950813 0.4172521 -1.0326400 0.8359434
## 605afa3a856f36729d2e5222  0.1033646340 0.4184323 -0.6885361 1.1228533
## 605c8bc6856f36729d2e5223 -0.1065917832 0.4213731 -1.1817585 0.6337637
## 605f3f2d856f36729d2e5224 -0.0016435313 0.4319396 -0.9221958 0.9319698
## 605f46c3856f36729d2e5225  0.0716357076 0.4086795 -0.7389420 1.0378890
## 60605337856f36729d2e5226 -0.0671044044 0.4020815 -1.0200615 0.7132768
## 60609ae6856f36729d2e5228 -0.0433234757 0.4057871 -0.9945799 0.8118748
## 6061ce91856f36729d2e522e  0.0156192045 0.4105734 -0.8765265 0.9472047
## 6061f106856f36729d2e5231  0.1856322445 0.4646586 -0.4845219 1.4078920
## 6068ea9f856f36729d2e523e -0.0884572608 0.4335679 -1.1844307 0.7224257
## 6075ab05856f36729d2e5247  0.0221584312 0.3876377 -0.7987899 0.8770786

Sampling plots

plot(scenario_quality3, ask = FALSE)

Posterior predictive check

pp_check(scenario_quality3, nsamples = 200, type = "bars")

Final model

All candidate models look nice, none is significantly better than the others, we will proceed the model containing work experince as it otherwise ourd be added in the next step: scenario_quality1

All data points

Some participants did only complete one scenario. Those has been excluded from the initial dataset to improve sampling of the models. We do however want to use all data we can and will therefore try to fit the model with the complete dataset.

scenario_quality1.all <- brm(
  "quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + (1 | session)",
  prior = c(
    prior(normal(0, 1), class = "b"),
    prior(normal(0, 2), class = "Intercept"),
    prior(exponential(1), class = "sd")
  ),
  family = cumulative(),
  data = as.data.frame(d.completed),
  file = "fits/scenario_quality1.all",
  file_refit = "on_change",
  seed = 20210421
)

Summary

summary(scenario_quality1.all)

##  Family: cumulative 
##   Links: mu = logit; disc = identity 
## Formula: quality_pre_task ~ 1 + high_debt_version + work_experience_programming.s + (1 | session) 
##    Data: as.data.frame(d.completed) (Number of observations: 51) 
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 4000
## 
## Group-Level Effects: 
## ~session (Number of levels: 29) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.32      0.27     0.01     1.00 1.00     2241     2352
## 
## Population-Level Effects: 
##                               Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept[1]                     -2.08      0.54    -3.21    -1.07 1.00
## Intercept[2]                     -0.59      0.40    -1.41     0.17 1.00
## Intercept[3]                      0.30      0.40    -0.50     1.07 1.00
## Intercept[4]                      0.81      0.41     0.01     1.62 1.00
## Intercept[5]                      2.07      0.47     1.16     3.04 1.00
## Intercept[6]                      4.21      0.74     2.92     5.77 1.00
## high_debt_versionfalse            1.41      0.47     0.48     2.33 1.00
## work_experience_programming.s    -0.52      0.27    -1.07    -0.01 1.00
##                               Bulk_ESS Tail_ESS
## Intercept[1]                      4447     3193
## Intercept[2]                      5947     3160
## Intercept[3]                      5162     3582
## Intercept[4]                      4933     3684
## Intercept[5]                      4327     3378
## Intercept[6]                      5201     3159
## high_debt_versionfalse            6359     3180
## work_experience_programming.s     5536     2843
## 
## Family Specific Parameters: 
##      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## disc     1.00      0.00     1.00     1.00 1.00     4000     4000
## 
## Samples were drawn using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Random effects

ranef(scenario_quality1.all)

## $session
## , , Intercept
## 
##                              Estimate Est.Error       Q2.5     Q97.5
## 6033c6fc5af2c702367b3a93 -0.030973291 0.3854158 -0.8996900 0.7694326
## 6033d69a5af2c702367b3a95 -0.136561496 0.3929666 -1.1464538 0.5008669
## 6033d90a5af2c702367b3a96 -0.018769543 0.3597007 -0.8408294 0.7565932
## 6034fc165af2c702367b3a98 -0.021765127 0.3477023 -0.8204487 0.7088100
## 603500725af2c702367b3a99  0.230712314 0.5017428 -0.4206965 1.6268883
## 603f84f15af2c702367b3a9b -0.009023566 0.3644119 -0.8182057 0.7776929
## 603f97625af2c702367b3a9d -0.042744744 0.3648838 -0.8926435 0.7129600
## 603fd5d95af2c702367b3a9e  0.012378490 0.3873651 -0.7956125 0.8910164
## 60409b7b5af2c702367b3a9f -0.047873935 0.3696613 -0.8868011 0.6597484
## 604b82b5a7718fbed181b336  0.185524519 0.4472555 -0.4606588 1.4185400
## 604f1239a7718fbed181b33f  0.022022527 0.3805942 -0.7979574 0.8850668
## 6050c1bf856f36729d2e5218 -0.078239995 0.3889774 -1.0521142 0.6543908
## 6050e1e7856f36729d2e5219  0.010330022 0.3942243 -0.8685386 0.8539365
## 6055fdc6856f36729d2e521b -0.013429287 0.3650010 -0.8338232 0.7646903
## 60579f2a856f36729d2e521e -0.132884272 0.4360632 -1.2761810 0.5667872
## 60589862856f36729d2e521f -0.025912696 0.3814473 -0.8940373 0.7428015
## 605a30a7856f36729d2e5221  0.089497327 0.4239680 -0.6657091 1.1517157
## 605afa3a856f36729d2e5222  0.090607496 0.3896112 -0.6174761 1.0546930
## 605c8bc6856f36729d2e5223 -0.104949142 0.4019869 -1.1328828 0.5965566
## 605f3f2d856f36729d2e5224  0.011176424 0.3847619 -0.8197675 0.9197957
## 605f46c3856f36729d2e5225  0.049884875 0.3684020 -0.6932974 0.9289825
## 60605337856f36729d2e5226 -0.076351740 0.3605451 -0.9599046 0.5831593
## 60609ae6856f36729d2e5228 -0.014562207 0.3768967 -0.8827419 0.7763377
## 6061ce91856f36729d2e522e -0.004064769 0.3649999 -0.7983834 0.7916814
## 6061f106856f36729d2e5231  0.176318266 0.4283463 -0.4299918 1.3526873
## 60672faa856f36729d2e523c -0.074094794 0.3890243 -1.0218945 0.7297932
## 6068ea9f856f36729d2e523e -0.052598797 0.3797743 -0.9990352 0.6790156
## 606db69d856f36729d2e5243 -0.052389636 0.3796989 -0.9876359 0.6870333
## 6075ab05856f36729d2e5247  0.054885087 0.3656994 -0.6145519 0.9511147

Sampling plots

plot(scenario_quality1.all, ask = FALSE)

Posterior predictive check

pp_check(scenario_quality1.all, nsamples = 200, type = "bars")

Final model

• Fitting the model to all data point did not significantly damage the model and will be used as is a more fair representation of reality.

This means that our final model, with all data points and experience predictors, is scenario_quality1.all

Interpreting the model

To begin interpreting the model we look at how it’s parameters were estimated. As our research is focused on how the outcome of the model is effected we will mainly analyze the \(\beta\) parameters.

\(\beta\) parameters

mcmc_areas(scenario_quality1.all, pars = c("b_high_debt_versionfalse", "b_work_experience_programming.s"), prob = 0.95) + scale_y_discrete() +
  scale_y_discrete(labels=c("High debt version: false", "Professional programming experience")) +
  ggtitle("Beta parameters densities in scenario quality model", subtitle = "Shaded region marks 95% of the density. Line marks the median")

scale_programming_experience <- function(x) {
  (x - mean(d.completed$work_experience_programming))/ sd(d.completed$work_experience_programming)
}
unscale_programming_experience <- function(x) {
  x * sd(d.completed$work_experience_programming) + mean(d.completed$work_experience_programming)
}

post_settings <- expand.grid(
  high_debt_version = c("false", "true"),
  session = NA,
  work_experience_programming.s = sapply(c(0, 3, 10, 25, 40), scale_programming_experience)
)

post <- posterior_predict(scenario_quality1.all, newdata = post_settings) %>%
  melt(value.name = "estimate", varnames = c("sample_number", "settings_id")) %>%
  left_join(
    rowid_to_column(post_settings, var= "settings_id"),
    by = "settings_id"
  ) %>%
  mutate(work_experience_programming = unscale_programming_experience(work_experience_programming.s)) %>%
  select(
    estimate,
    high_debt_version,
    work_experience_programming
  )%>%
  mutate(estimate = estimate)

post.nice <- post %>%  mutate_at("estimate", function(x) revalue(as.ordered(x), c(
      "1"="Very Bad",
      "2"="Bad",
      "3"="Somewhat Bad",
      "4"="Neutral",
      "5"="Somewhat Good",
      "6"="Good",
      "7"="Very Good"
    )))

data.frame(
  High_debt_version_3_years = post.nice %>%
    filter(high_debt_version == "true", work_experience_programming == 3) %>%
    pull(estimate),
  Low_debt_version_3_years = post.nice %>%
    filter(high_debt_version == "false", work_experience_programming == 3) %>%
    pull(estimate)
) %>%
  likert() %>%
  plot(
    type="density",
    facet = TRUE,
  )

data.frame(
  High_debt_version_25_years = post.nice %>%
    filter(high_debt_version == "true", work_experience_programming == 25) %>%
    pull(estimate),
  Low_debt_version_25_years = post.nice %>%
    filter(high_debt_version == "false", work_experience_programming == 25) %>%
    pull(estimate)
) %>%
  likert() %>%
  plot(
    type="density",
    facet = TRUE,
  )

post.diff <- post %>% filter(high_debt_version == "true")
post.diff$estimate = post.diff$estimate -  filter(post, high_debt_version == "false")$estimate

post.diff %>%
  ggplot(aes(x=estimate)) +
  geom_boxplot(quantile_lines = TRUE, quantile_fun = hdi, vline_linetype = 2) +
  facet_grid(rows = vars(work_experience_programming)) +
  labs(
    title = "Scenario rating diff / years of programming experience",
    subtitle = "Difference as: high debt rating - low debt rating",
    x = "Rating difference"
  ) +
  scale_y_continuous(breaks = NULL)

We can then proceed to calculate some likelihoods:

post.diff.10 <- post.diff %>% filter(work_experience_programming == 10)
high_debt_rated_higher <- sum(post.diff.10$estimate > 0)
low_debt_rated_higher <- sum(post.diff.10$estimate < 0)
low_debt_rated_higher / high_debt_rated_higher

## [1] 2.910776

Participants with 10 years of professional programming experience were 203% more likely to rate the high debt version scenario as worse than then they were to rate the low debt version scenario as worse.